Political Donations of Professional Sports Team Owners
Owners Who Donate The Most Frequently and How Much
Top 25 Highest Political Donations by NFL Team Owners in 2016
Blockbusters
## # A tibble: 430 × 13
## release_year rank_in_year imdb_rating mpaa_rating film_title film_budget
## <dbl> <dbl> <dbl> <chr> <chr> <dbl>
## 1 2019 1 8.5 PG-13 Avengers: Endg… 356000000
## 2 2019 2 7 PG The Lion King 260000000
## 3 2019 3 7.2 PG Frozen II 150000000
## 4 2019 4 7.6 PG-13 Spider-Man: Fa… 160000000
## 5 2019 5 6.9 PG-13 Captain Marvel 175000000
## 6 2019 6 7.9 G Toy Story 4 200000000
## 7 2019 7 8.6 R Joker 55000000
## 8 2019 8 7 PG Aladdin 183000000
## 9 2019 9 6.9 PG-13 Star Wars: Epi… 200000000
## 10 2019 10 6.5 PG-13 Fast & Furious… 200000000
## # … with 420 more rows, and 7 more variables: length_in_min <dbl>,
## # domestic_distributor <chr>, worldwide_gross <dbl>, domestic_gross <dbl>,
## # genre_1 <chr>, genre_2 <chr>, genre_3 <chr>
5 Highest & Lowest IMBD Ratings Highlighted by Title
## # A tibble: 5 × 2
## mpaa_rating n
## <chr> <int>
## 1 G 23
## 2 PG 133
## 3 PG-13 178
## 4 R 95
## 5 <NA> 1
History of Rock
## # A tibble: 5,484 × 18
## index name artist release_date length popularity danceability...7
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 0 Smells Like T… Nirvana 1991 5.03 74 0.502
## 2 1 Stairway to H… Led Zep… 1971 8.05 78 0.338
## 3 2 Bohemian Rhap… Queen 1975 5.91 74 0.392
## 4 3 Imagine - Rem… John Le… 1971 3.13 77 0.547
## 5 4 (I Can't Get … The Rol… 1965 3.71 77 0.723
## 6 5 Hotel Califor… Eagles 1976 6.52 83 0.579
## 7 6 Enter Sandman Metalli… 1991 5.53 74 0.579
## 8 7 Whole Lotta L… Led Zep… 1969 5.56 77 0.412
## 9 8 Comfortably N… Pink Fl… 1979 6.37 74 0.472
## 10 9 One U2 1991 4.60 76 0.392
## # … with 5,474 more rows, and 11 more variables: acousticness <dbl>,
## # danceability...9 <dbl>, energy <dbl>, instrumentalness <dbl>, key <dbl>,
## # liveness <dbl>, loudness <dbl>, speechiness <dbl>, tempo <dbl>,
## # time_signature <dbl>, valence <dbl>
Most Popular Nirvana Songs
history_of_rock %>% group_by(artist) %>%
summarise(n = n()) %>% arrange(desc(n))
## # A tibble: 1,738 × 2
## artist n
## <chr> <int>
## 1 Various Artists 116
## 2 The Beatles 84
## 3 The Rolling Stones 58
## 4 Led Zeppelin 52
## 5 AC/DC 43
## 6 Bruce Springsteen 42
## 7 Elvis Presley 33
## 8 Pink Floyd 31
## 9 Bob Dylan 29
## 10 Metallica 29
## # … with 1,728 more rows
nirvana <- history_of_rock %>% filter(artist == "Nirvana") %>% arrange(desc(popularity)) %>%
mutate(order = fct_reorder(.f = name, .x = popularity)) %>%
rename(Release_Date = 'release_date')
nirvana %>% select(order, everything())
## # A tibble: 19 × 19
## order index name artist Release_Date length popularity danceability...7
## <fct> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Heart-S… 270 Heart-… Nirva… 1993 4.69 75 0.256
## 2 Smells … 0 Smells… Nirva… 1991 5.03 74 0.502
## 3 The Man… 497 The Ma… Nirva… 1994 4.35 74 0.483
## 4 Come As… 37 Come A… Nirva… 1991 3.65 72 0.5
## 5 About A… 371 About … Nirva… 2002 2.78 70 0.409
## 6 All Apo… 194 All Ap… Nirva… 1993 3.89 69 0.446
## 7 Lithium 169 Lithium Nirva… 1991 4.28 68 0.678
## 8 Lake Of… 723 Lake O… Nirva… 1994 2.93 66 0.541
## 9 In Bloo… 155 In Blo… Nirva… 1991 4.25 65 0.436
## 10 Rape Me 687 Rape Me Nirva… 1993 2.83 64 0.42
## 11 Dumb 1325 Dumb Nirva… 1993 2.53 64 0.697
## 12 Drain Y… 3015 Drain … Nirva… 1991 3.73 60 0.325
## 13 Breed 742 Breed Nirva… 1991 3.07 59 0.262
## 14 Polly 1103 Polly Nirva… 1991 2.90 58 0.83
## 15 Sliver 2642 Sliver Nirva… 1992 2.27 58 0.428
## 16 Pennyro… 3902 Pennyr… Nirva… 1993 3.65 58 0.428
## 17 On A Pl… 2416 On A P… Nirva… 1991 3.24 54 0.428
## 18 Verse C… 3117 Verse … Nirva… 1991 3.21 35 0.529
## 19 Rainbow… 2914 Rainbo… Nirva… 2018 2.64 25 0.499
## # … with 11 more variables: acousticness <dbl>, danceability...9 <dbl>,
## # energy <dbl>, instrumentalness <dbl>, key <dbl>, liveness <dbl>,
## # loudness <dbl>, speechiness <dbl>, tempo <dbl>, time_signature <dbl>,
## # valence <dbl>
plot3 <- ggplot(data = nirvana, aes(x = popularity, y = order, label = Release_Date)) +
geom_point() +
geom_segment(aes(x = popularity, xend = popularity, y = 0, yend = order)) +
coord_flip() +
labs(x = "Popularity",
y = "Song Title",
title = "Most Popular Nirvana Songs")
ggplotly(plot3, tooltip = "label")
Unemployment
unemployment_data_us <- read_csv("data/unemployment_data_us.csv")
## Rows: 132 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Month, Date
## dbl (11): Year, Primary_School, High_School, Associates_Degree, Professional...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unemployment_data_us
## # A tibble: 132 × 13
## Year Month Primary_School Date High_School Associates_Degree
## <dbl> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2010 Jan 15.3 Jan-2010 10.2 8.6
## 2 2011 Jan 14.3 Jan-2011 9.5 8.1
## 3 2012 Jan 13 Jan-2012 8.5 7.1
## 4 2013 Jan 12 Jan-2013 8.1 6.9
## 5 2014 Jan 9.4 Jan-2014 6.5 5.9
## 6 2015 Jan 8.3 Jan-2015 5.4 5.2
## 7 2016 Jan 7.1 Jan-2016 5.2 4.2
## 8 2017 Jan 7.4 Jan-2017 5.2 3.8
## 9 2018 Jan 5.4 Jan-2018 4.4 3.4
## 10 2019 Jan 5.7 Jan-2019 3.7 3.4
## # … with 122 more rows, and 7 more variables: Professional_Degree <dbl>,
## # White <dbl>, Black <dbl>, Asian <dbl>, Hispanic <dbl>, Men <dbl>,
## # Women <dbl>